Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.

Journal: Skeletal radiology
PMID: 38937291

Abstract

OBJECTIVE: To develop a whole-body low-dose CT (WBLDCT) deep learning model and determine its accuracy in predicting the presence of cytogenetic abnormalities in multiple myeloma (MM).

Authors

  • Shahriar Faghani
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Mana Moassefi
    Mayo Clinic Artificial Intelligence Lab, Department of Radiology, Mayo Clinic, 200 1st Street, S.W., Rochester, MN, 55905, USA.
  • Udit Yadav
    Division of Hematology, Mayo Clinic, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA.
  • Francis K Buadi
    Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
  • Shaji K Kumar
    Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Wilson I Gonsalves
    Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA.
  • Francis I Baffour
    Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

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